Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "79" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 79 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 77 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459896 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.840138 | -0.498024 | -0.613853 | -0.915752 | -1.874349 | -2.650435 | 1.509475 | -0.687700 | 0.6363 | 0.6640 | 0.3970 | nan | nan |
| 2459894 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.130204 | -0.376590 | -0.593255 | -1.032021 | -0.573885 | -1.491735 | 0.084798 | -0.747141 | 0.6426 | 0.6619 | 0.3837 | nan | nan |
| 2459893 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.921710 | -0.582381 | -0.764507 | -1.166526 | -0.898220 | -2.139604 | -0.470300 | -1.182336 | 0.6424 | 0.6641 | 0.3868 | nan | nan |
| 2459892 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.872537 | -0.458607 | -0.845787 | -1.145299 | -1.109127 | -1.305993 | -0.628344 | -0.975052 | 0.6363 | 0.6653 | 0.3919 | nan | nan |
| 2459891 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.845845 | -0.370037 | -0.517770 | -0.979751 | -1.348090 | -1.908343 | -0.702511 | -0.917191 | 0.6272 | 0.6557 | 0.3957 | nan | nan |
| 2459890 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.583442 | -0.287627 | -0.963258 | -1.037418 | -0.530615 | -1.578544 | 1.016450 | -0.208876 | 0.6286 | 0.6504 | 0.3906 | nan | nan |
| 2459889 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.189549 | -0.334828 | -0.605636 | -1.055580 | -1.208098 | -2.218922 | -0.765392 | -1.264878 | 0.6403 | 0.6582 | 0.3887 | nan | nan |
| 2459888 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.999415 | -0.358451 | -0.782301 | -0.870218 | -0.830040 | -2.090405 | -0.712424 | -1.180633 | 0.6573 | 0.6792 | 0.3826 | nan | nan |
| 2459887 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.421122 | -0.437456 | -2.371212 | -1.611595 | 44.236244 | -2.373983 | 10.709186 | -0.654368 | 0.6191 | 0.6592 | 0.4032 | nan | nan |
| 2459886 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.112917 | -0.758264 | -0.365771 | -0.807239 | 4.888402 | 5.464460 | -0.533863 | -0.861695 | 0.7146 | 0.7128 | 0.3486 | nan | nan |
| 2459885 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.758250 | 1.475686 | 15.690326 | 16.726479 | 2.380642 | 3.322279 | 5.579992 | 2.698220 | 0.6859 | 0.6944 | 0.3574 | nan | nan |
| 2459884 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.985832 | 2.547526 | 3.584384 | 3.479950 | 2.441919 | 2.746770 | -2.280111 | -2.905021 | 0.6525 | 0.6600 | 0.3978 | nan | nan |
| 2459883 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.241497 | 5.069555 | 34.674780 | 35.857744 | 3.434674 | 5.901816 | -3.747497 | -4.485112 | 0.6547 | 0.6701 | 0.3899 | nan | nan |
| 2459882 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 6.479226 | 7.727831 | 40.761546 | 41.602499 | 5.393095 | 7.944029 | -1.213309 | -1.925330 | 0.6578 | 0.6711 | 0.3862 | nan | nan |
| 2459881 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.084973 | 5.078929 | 45.832566 | 47.176684 | 9.273427 | 14.724296 | -3.966211 | 1.731650 | 0.7027 | 0.7201 | 0.3296 | nan | nan |
| 2459880 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.491047 | 5.500322 | 36.832006 | 38.278449 | 2.988116 | 4.996344 | -1.736557 | -2.912429 | 0.6528 | 0.6687 | 0.3988 | nan | nan |
| 2459879 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.948934 | 1.555236 | 0.931596 | 0.824407 | -1.323913 | -1.548838 | -2.605009 | -3.454105 | 0.6362 | 0.6592 | 0.4164 | nan | nan |
| 2459878 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.547323 | 5.511610 | 44.915192 | 46.754884 | 5.254896 | 8.393276 | -1.114281 | -5.763563 | 0.6485 | 0.6728 | 0.4027 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | ee Temporal Discontinuties | 1.509475 | -0.498024 | 0.840138 | -0.915752 | -0.613853 | -2.650435 | -1.874349 | -0.687700 | 1.509475 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | ee Shape | 1.130204 | -0.376590 | 1.130204 | -1.032021 | -0.593255 | -1.491735 | -0.573885 | -0.747141 | 0.084798 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | ee Shape | 0.921710 | 0.921710 | -0.582381 | -0.764507 | -1.166526 | -0.898220 | -2.139604 | -0.470300 | -1.182336 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | ee Shape | 0.872537 | -0.458607 | 0.872537 | -1.145299 | -0.845787 | -1.305993 | -1.109127 | -0.975052 | -0.628344 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | ee Shape | 0.845845 | 0.845845 | -0.370037 | -0.517770 | -0.979751 | -1.348090 | -1.908343 | -0.702511 | -0.917191 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | ee Shape | 1.583442 | -0.287627 | 1.583442 | -1.037418 | -0.963258 | -1.578544 | -0.530615 | -0.208876 | 1.016450 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | ee Shape | 1.189549 | 1.189549 | -0.334828 | -0.605636 | -1.055580 | -1.208098 | -2.218922 | -0.765392 | -1.264878 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | ee Shape | 0.999415 | -0.358451 | 0.999415 | -0.870218 | -0.782301 | -2.090405 | -0.830040 | -1.180633 | -0.712424 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | ee Temporal Variability | 44.236244 | -0.437456 | 1.421122 | -1.611595 | -2.371212 | -2.373983 | 44.236244 | -0.654368 | 10.709186 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | nn Temporal Variability | 5.464460 | 1.112917 | -0.758264 | -0.365771 | -0.807239 | 4.888402 | 5.464460 | -0.533863 | -0.861695 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | nn Power | 16.726479 | 1.475686 | 3.758250 | 16.726479 | 15.690326 | 3.322279 | 2.380642 | 2.698220 | 5.579992 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | ee Power | 3.584384 | 2.547526 | 1.985832 | 3.479950 | 3.584384 | 2.746770 | 2.441919 | -2.905021 | -2.280111 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | nn Power | 35.857744 | 5.069555 | 4.241497 | 35.857744 | 34.674780 | 5.901816 | 3.434674 | -4.485112 | -3.747497 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | nn Power | 41.602499 | 7.727831 | 6.479226 | 41.602499 | 40.761546 | 7.944029 | 5.393095 | -1.925330 | -1.213309 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | nn Power | 47.176684 | 5.078929 | 4.084973 | 47.176684 | 45.832566 | 14.724296 | 9.273427 | 1.731650 | -3.966211 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | nn Power | 38.278449 | 5.500322 | 4.491047 | 38.278449 | 36.832006 | 4.996344 | 2.988116 | -2.912429 | -1.736557 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | nn Shape | 1.555236 | 1.555236 | 0.948934 | 0.824407 | 0.931596 | -1.548838 | -1.323913 | -3.454105 | -2.605009 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | N11 | not_connected | nn Power | 46.754884 | 5.511610 | 4.547323 | 46.754884 | 44.915192 | 8.393276 | 5.254896 | -5.763563 | -1.114281 |